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2006.02243
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The Value-Improvement Path: Towards Better Representations for Reinforcement Learning
AAAI Conference on Artificial Intelligence (AAAI), 2020
3 June 2020
Will Dabney
André Barreto
Mark Rowland
Robert Dadashi
John Quan
Marc G. Bellemare
David Silver
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Papers citing
"The Value-Improvement Path: Towards Better Representations for Reinforcement Learning"
50 / 52 papers shown
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Maximum Entropy Model Correction in Reinforcement Learning
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Kimberly L. Stachenfeld
298
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Small batch deep reinforcement learning
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Elephant Neural Networks: Born to Be a Continual Learner
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Rasool Fakoor
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260
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TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
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Bootstrapped Representations in Reinforcement Learning
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Stephen Tu
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The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
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208
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Deep Reinforcement Learning with Plasticity Injection
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300
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Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
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Yash Chandak
S. Thakoor
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Will Dabney
Diana Borsa
292
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Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
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Joshua Greaves
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Charline Le Lan
Ross Goroshin
Pablo Samuel Castro
Marc G. Bellemare
190
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Hao Fei
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227
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02 Mar 2023
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces
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Joshua Greaves
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206
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207
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239
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Sparsity Inducing Representations for Policy Decompositions
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H. Geyer
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Geometric Policy Iteration for Markov Decision Processes
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196
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Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning
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The Primacy Bias in Deep Reinforcement Learning
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Max Schwarzer
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Understanding and Preventing Capacity Loss in Reinforcement Learning
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122
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The Geometry of Robust Value Functions
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Simon Osindero
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185
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